Adaptive wavelet packet based image coding with optimal entropy-constrained lattice vector quantizer (ECLVQ)

Kim, Wonha; Nguyen, Truong Q.; Hu, Yu-Hen

doi:10.1109/78.700974

Cited by 4 publications

References 10 publications

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Image denoising algorithm via best wavelet packet base using Wiener cost function

Shui

Zhou

2007

IET Image Process.

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A new cost function, namely, the Wiener cost function, is introduced to find the best wavelet packet (WP) base in image denoising. Unlike the existing entropy-type cost functions in image compression, the Wiener cost function depends on both sparseness of image representation and noise level. Combining the Wiener cost function and the doubly local Wiener filtering scheme, a new image denoising algorithm is proposed using the best wavelet packet bases. Owing to unknown true image in denoising, a pilot image with less noise is required to find the best wavelet packet base, which is obtained by the existing denoising algorithms. From the pilot image, the best 2D wavelet packet tree is searched in terms of the Wiener cost function and the energy distributions of the image in the best wavelet packet domain are also estimated. Further, the image is recovered by applying the local Wiener filtering to the best wavelet packet coefficients of the noisy image. The experimental results show that for images of structural textures, for example 'Barbara' and texture images, the proposed algorithm greatly improves denoising performance as compared with the existing state-of-the-art algorithms.

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Image denoising algorithm via best wavelet packet base using Wiener cost function

Shui

Zhou

2007

IET Image Process.

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Estimation of the stapes-bone thickness in the stapedotomy surgical procedure using a machine-learning technique

Kaburlasos

Petridis

Brett

et al. 1999

IEEE Trans. Inform. Technol. Biomed.

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Stapedotomy is a surgical procedure aimed at the treatment of hearing impairment due to otosclerosis. The treatment consists of drilling a hole through the stapes bone in the inner ear in order to insert a prosthesis. Safety precautions require knowledge of the nonmeasurable stapes thickness. The technical goal herein has been the design of high-level controls for an intelligent mechatronics drilling tool in order to enable the estimation of stapes thickness from measurable drilling data. The goal has been met by learning a map between drilling features, hence no model of the physical system has been necessary. Learning has been achieved as explained in this paper by a scheme, namely the d-sigma Fuzzy Lattice Neurocomputing (d sigma-FLN) scheme for classification, within the framework of fuzzy lattices. The successful application of the d sigma-FLN scheme is demonstrated in estimating the thickness of a stapes bone "on-line" using drilling data obtained experimentally in the laboratory.

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Adaptive subband image coding considering spatial relationship on hierarchical pyramid

Otake

Kawanaka²

Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348)

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Adaptive wavelet packet based image coding with optimal entropy-constrained lattice vector quantizer (ECLVQ)

Cited by 4 publications

References 10 publications

Image denoising algorithm via best wavelet packet base using Wiener cost function

Image denoising algorithm via best wavelet packet base using Wiener cost function

Estimation of the stapes-bone thickness in the stapedotomy surgical procedure using a machine-learning technique

Adaptive subband image coding considering spatial relationship on hierarchical pyramid

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